The present invention concerns the processing of a digital object.
A digital object consists of elements, that is to say elementary items of information. It may be of various natures. It may for example be a bidimensional image in the case of digital photography in particular. In this case, the elements constituting such an image are pixels (standing for “picture elements”).
In a variant, the object may be a sequence of bidimensional images, which can also be seen as a three-dimensional image (two spatial dimensions and one time dimension). This is the case for example in certain medical applications or for films. The elements constituting such an object are for example the pixels of an image in the sequence of images.
The digital object in question may also possess four dimensions, as is the case for films where each image is three dimensional for example.
According to another example, the digital object could comprise a sound, the constituent elements of which are for example intensities, or a modulated signal the constituent elements of which may correspond to intensities and/or phases.
More generally, a digital object has one or more dimensions among the following dimensions: spatial (for example a distance, an angle or a travel in a mesh), temporal, frequential (for example a colour, a frequency, a frequency band), a phase, a decomposition according to another vectorial space base (for example a wavelet decomposition), or any dimension of any topological space.
In addition to the elementary information making it up, a digital object comprises a certain number of attributes, which may vary according to its nature. These attributes may in particular relate to the dimensions of the object and to the elementary information. It is then possible to define types of object, which each correspond to objects having a given set of attributes. Among the typical attributes of an object, the colour, geometry and definition can for example be cited. Other attributes can of course also be envisaged. Examples of types of objects and attributes are provided below.
When the digital object in question is a digital image, this can be obtained for example by means of an acquisition system, such as a photographing system.
According to non-limitative examples, the acquisition system may be a disposable photographic apparatus, a digital photographic apparatus, a reflex camera (digital or not), a scanner, a fax, an endoscope, a camera, a camcorder, a surveillance camera, a toy, a camera or photographic apparatus integrated in or connected to a telephone, a personal assistant or a computer, a thermal camera, an echographic apparatus, an MRI (magnetic resonance) imaging apparatus, or an x-ray radiography apparatus.
An image acquisition system generally comprises, apart from an optical system the role of which is to focus the light, a sensor. Such a sensor comprises mechanical, chemical or electronic means for capturing and/or recording images.
The sensor is for example a system of photosensitive cells that transforms the quantity of light received into digital values, and attributes to each pixel the value or values that correspond thereto. The raw image directly acquired by the sensor is traditionally called the RAW image. The number of digital values finally attributed to each pixel depends on the photographic system.
For various reasons, the values of the elementary items of information of a RAW image returned by the sensor are not functions completely deterministic of the quantity of light received. They contain a random part, called noise, which has no relationship to the scene observed. The noise is for example due to the particular character of the light or to thermal phenomena taking place in the electronic circuitry of the sensor.
Some of the noise is generally extremely local, and its extent may be of the order of magnitude of 1 pixel.
As a first approximation, its statistical properties are well described by two parameters:                a distance that gives the characteristic distance that it is necessary to travel for two values of the noise to be statistically independent or the dependency of which is below a predetermined threshold. This distance can be seen as the size of the digital grain, a definition of which will be given below, and        a value called the intensity, which describes the variation in the digital values caused by the noise. In the case of a colour image, the noise has value on each channel.        
Within the meaning of the invention, a white noise or more generally a “white” object is defined as an object for which the elementary items of information at a position of the object are decorrelated from the elementary items of information of this same object in adjoining positions. The elementary values of the object are therefore statistically independent.
Within the meaning of the invention, a “quasi-white” noise or more generally a “quasi-white” object is defined as an object for which the elementary items of information at a position of the object have a correlation level, with elementary items of information of this same object at adjoining positions, lower than a threshold; this threshold being either predetermined for example according to the sensitivity of the eye to the structured noise, or determined from the autocorrelation of another object; thus this threshold may for example take the value 10%; this threshold may also take for example the value 10% of the autocorrelation level of the original object from which the quasi-white object is extracted by one of the methods according to the invention.
In the same way, an image other than a RAW image, that is to say one having already undergone a certain number of transformations, contains noise.
Before being displayed, a digital image undergoes a succession of processing operations, referred to as a processing string. The purpose of these processing operations is for example to improve the definition, to eliminate artefacts or to modify the colour.
Among these processing operations, some may have a detrimental effect on the noise contained in the image in question.
This is the case in particular with so-called neighbouring processing operations, in the sense that they transform the values of a pixel (or more generally of an object element) according to the values of adjoining pixels (or more generally adjoining object elements).
These neighbouring processing operations use the fact that close positions in a scene contain correlated information, that is to say linked to each other. This assumption makes it possible to reconstruct the missing or degraded information in the digital images.
Not only are the values in each pixel liable to be modified, but new values in each pixel may be created. The final image may therefore have a different number of channels from that of the original image. For example, when the original image is a RAW image, with a single value per pixel corresponding to a single channel, the final image, after processing, may have three values per pixel corresponding respectively to the three red, green and blue (R,G,B) channels.
One problem posed by these neighbouring processing operations is that the assumption of local correlation of the information that they make is wrong for noise, which is of a nature independent of the physical properties of the scene.
More precisely, a neighbouring processing will create correlations in the noise having as their consequence the appearance of visible structures that in no way correspond to the scene observed. In other words, a neighbouring processing structures the noise. From a mathematical point of view, neighbouring processing operations change the statistical properties of the noise, such as the autocorrelation function.
This structuring of the noise is represented visually by the appearance of a digital grain. In the case of a colour image, this grain typically appears in the form of ugly coloured spots, the size and intensity of which depend on the neighbouring processing operations. This digital grain does not in general have the charm of that of silver photography, and it is wished to be able to dispense with it.
One possible definition for the digital grain would for example be the autocorrelation of the noise that quantifies the spatial correlation of the noise with itself, taken at different spatial positions.
Another example of processing operations that may have a detrimental effect on the noise contained in a digital image concerns the processing operations tending to amplify the noise. These processing operations may cause a degradation of the image since the noise is more visible therein, sometimes to the detriment of the useful information.
One example of such processing operations is the increase in the contrast of an image. It consists schematically of making the dark pixels in an image darker and the light pixels of the image lighter. This amplification of the contrast applies in the same way to the noise contained in the processed image.
In summary, the processing operations that structure or amplify the noise create undesirable effects when they are applied to a noisy digital image.
One idea for attempting to limit these undesirable effects would be to denoise the digital image before applying to it the processing operations in question. This idea was in particular envisaged in U.S. Pat. No. 6,934,056, in a specific application. This document makes provision in fact for denoising a RAW image before applying to it the neighbouring processing known as dematrixing.
Various denoising algorithms can be used for this purpose. The purpose thereof is to modify the original image in order to derive from it, ideally, an image that would be the one that would be obtained in the absence of noise.
However, even the best current denoising methods do not make it possible to distinguish very fine textures, such as the roughness of a surface or the irregularities of a skin, and noise. The majority of these methods have the effect of reducing the intensity of the noise to the detriment of the fineness of its grain.
Because of this, the denoising of a digital image, while attenuating its noise, eliminates certain fine structures that constitute the useful information of the image. The image that results therefrom may give the impression of lacking relief and realism.
Although the example of a digital image has been examined above in particular, it should be noted that the same problem is posed in a similar fashion for the other kinds of object mentioned above.
One object of the present invention is to limit the drawbacks stated above.
In particular, one object of the invention is to limit the structuring of the noise contained in a digital object, without causing excessive loss of useful information.